{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析训练集中这四个指标的平稳性，对于不平稳的，要对其进行差分运算，使差分后的序列变得平稳，然后才能建立VAR模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "指标 e 单位根检验的p值为：  0.8663327869244546\n",
      "指标 prod 单位根检验的p值为：  0.8586871782128878\n",
      "指标 rw 单位根检验的p值为：  0.0013531962631896082\n",
      "指标 U 单位根检验的p值为：  0.11133330647767575\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "src_canada = pd.read_csv(\"http://image.cador.cn/data/canada.csv\")\n",
    "val_columns = ['e','prod','rw','U']\n",
    "v_std = src_canada[val_columns].apply(lambda x:np.std(x)).values\n",
    "v_mean = src_canada[val_columns].apply(lambda x:np.mean(x)).values\n",
    "canada = src_canada[val_columns].apply(lambda x:(x-np.mean(x))/np.std(x))\n",
    "train = canada.iloc[0:-8]\n",
    "\n",
    "import statsmodels.tsa.stattools as stat\n",
    "for col in val_columns:\n",
    "    pvalue = stat.adfuller(train[col],1)[1]\n",
    "    print(\"指标\",col,\"单位根检验的p值为： \",pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "进一步进行差分运算（为便于处理，这里对四个指标同时进行差分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "指标 e 单位根检验的p值为：  0.0004156438933942739\n",
      "指标 prod 单位根检验的p值为：  1.2492333501288617e-08\n",
      "指标 rw 单位根检验的p值为：  4.5335282339883554e-06\n",
      "指标 U 单位根检验的p值为：  0.00023868665380719157\n"
     ]
    }
   ],
   "source": [
    "# 由于这四个指标都不平稳，因此需要进行合适的差分运算\n",
    "train_diff = train.apply(lambda x: np.diff(x),axis=0)\n",
    "\n",
    "for col in val_columns:\n",
    "    pvalue = stat.adfuller(train_diff[col],1)[1]\n",
    "    print(\"指标\",col,\"单位根检验的p值为： \",pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们使用最小二乘法，求解每个方程的系数，并通过逐渐增加阶数，为模型定阶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P</th>\n",
       "      <th>AIC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>-19.996796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>-17.615455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>-9.407306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>6.907540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>34.852248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>77.620404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>138.382810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>220.671801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>328.834718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>466.815468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    P         AIC\n",
       "0   1  -19.996796\n",
       "1   2  -17.615455\n",
       "2   3   -9.407306\n",
       "3   4    6.907540\n",
       "4   5   34.852248\n",
       "5   6   77.620404\n",
       "6   7  138.382810\n",
       "7   8  220.671801\n",
       "8   9  328.834718\n",
       "9  10  466.815468"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模型阶数从1开始逐一增加\n",
    "rows, cols = train_diff.shape\n",
    "aicList = []\n",
    "lmList = []\n",
    "\n",
    "for p in range(1,11):\n",
    "    baseData = None\n",
    "    for i in range(p,rows):\n",
    "        tmp_list = list(train_diff.iloc[i]) + list(train_diff.iloc[i-p:i].values.flatten())\n",
    "        if baseData is None:\n",
    "            baseData = [tmp_list]\n",
    "        else:\n",
    "            baseData = np.r_[baseData, [tmp_list]]\n",
    "    X = np.c_[[1]*baseData.shape[0],baseData[:,cols:]]\n",
    "    Y = baseData[:,0:cols]\n",
    "    coefMatrix = np.matmul(np.matmul(np.linalg.inv(np.matmul(X.T,X)),X.T),Y)\n",
    "    aic = np.log(np.linalg.det(np.cov(Y - np.matmul(X,coefMatrix),rowvar=False))) + 2*(coefMatrix.shape[0]-1)**2*p/baseData.shape[0]\n",
    "    aicList.append(aic)\n",
    "    lmList.append(coefMatrix)\n",
    "\n",
    "#对比查看阶数和AIC\n",
    "pd.DataFrame({\"P\":range(1,11),\"AIC\":aicList})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "基于该模型，对未来8期的数据进行预测，并与验证数据集进行比较分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.20065717, -0.7208273 ,  0.08095578, -0.18725653],\n",
       "       [ 0.03650856, -0.08061888,  0.05900709, -0.22667618],\n",
       "       [ 0.03751544, -0.87174186,  0.17291551,  0.10381011],\n",
       "       [-0.04826459, -0.06498827,  0.45879439,  0.34885492],\n",
       "       [-0.15647981, -0.6096229 , -1.1219943 , -0.12520269],\n",
       "       [ 0.51480518, -0.51864268,  0.7123945 , -0.2760806 ],\n",
       "       [ 0.32312138, -0.06077591, -0.14816924, -0.39923473],\n",
       "       [-0.34031027,  0.78080541,  1.31294708,  0.01779691]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = np.argmin(aicList)+1\n",
    "n = rows\n",
    "preddf = None\n",
    "for i in range(8):\n",
    "    predData = list(train_diff.iloc[n+i-p:n+i].values.flatten())\n",
    "    predVals = np.matmul([1]+predData,lmList[p-1])\n",
    "    # 使用逆差分运算，还原预测值\n",
    "    predVals=train.iloc[n+i,:]+predVals\n",
    "    if preddf is None:\n",
    "        preddf = [predVals]\n",
    "    else:\n",
    "        preddf = np.r_[preddf, [predVals]]\n",
    "        \n",
    "    # 为train增加一条新记录\n",
    "    train = train.append(canada[n+i+1:n+i+2],ignore_index=True)\n",
    "    # 为train_diff增加一条新记录\n",
    "    df = pd.DataFrame(list(canada[n+i+1:n+i+2].values - canada[n+i:n+i+1].values), columns=canada.columns)\n",
    "    train_diff = train_diff.append(df,ignore_index=True)\n",
    "    \n",
    "preddf = preddf*v_std + v_mean\n",
    "\n",
    "#分析预测残差情况\n",
    "preddf - src_canada[canada.columns].iloc[-8:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    32.000000\n",
       "mean      0.799252\n",
       "std       1.551933\n",
       "min       0.003811\n",
       "25%       0.018936\n",
       "50%       0.111144\n",
       "75%       0.264179\n",
       "max       5.760963\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#统计预测百分误差率分布\n",
    "pd.Series((np.abs(preddf - src_canada[canada.columns].iloc[-8:].values)*100/src_canada[canada.columns].iloc[-8:].values).flatten()).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "绘制二维图表观察预测数据与真实数据的逼近情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "# 以下 font.family 设置仅适用于 Mac系统，其它系统请使用对应字体名称\n",
    "matplotlib.rcParams['font.family'] = 'Arial Unicode MS'\n",
    "m = 16\n",
    "xts = src_canada[['year','season']].iloc[-m:].apply(lambda x:str(x[0])+'-'+x[1],axis=1).values\n",
    "fig, axes = plt.subplots(2,2,figsize=(10,7))\n",
    "index = 0\n",
    "for ax in axes.flatten():\n",
    "    ax.plot(range(m),src_canada[canada.columns].iloc[-m:,index],'-',c='lightgray',linewidth=2,label=\"real\")\n",
    "    ax.plot(range(m-8,m),preddf[:,index],'o--',c='black',linewidth=2,label=\"predict\")\n",
    "    ax.set_xticklabels(xts,rotation=50)\n",
    "    ax.set_ylabel(\"$\"+canada.columns[index]+\"$\",fontsize=14)\n",
    "    ax.legend()\n",
    "    index = index + 1\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
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